Relevant bibliographies by topics / Computational Cost Reduction

Academic literature on the topic 'Computational Cost Reduction'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Computational Cost Reduction"

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Et al., Kiattikul Sooksomsatarn. "Computational Cost Reduction of Transaction Signing in Blockchain." Psychology and Education Journal 58, no. 1 (January 29, 2021): 1496–500. http://dx.doi.org/10.17762/pae.v58i1.935.

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Nowadays, Blockchain is a disruptive technology, particularly in the financial context. Moreover, Blockchain is behind the success of cryptocurrencies, e.g., Bitcoin and Ethereum. Unlike traditional currencies, cryptocurrencies are entirely virtual. There is no physical money, but it can directly make payments in digital currency from one person to another without intermediaries. Moreover, Hashing's cryptographic algorithm makes Blockchain resist tampering from any transacting participants because the submitted block cannot be altered or re-engineered. However, another big problem is how users of cryptocurrencies stop somebody from adding or editing a transaction that spends someone else's money to them. To do this, Blockchain needs another cryptosystem called Public/Private Keys, a primitive asymmetric cryptosystem, e.g., the RSA encryption, to sign the transactions for proving the authenticity of the ownership without revealing the signed secret information. The generated public key is regarded as a ledger account number or digital wallet of the sender and the recipient. Simultaneously, the paired private keys are used to identify whether the digital wallets' owners are authentic. As growing network entities and propagated Blockchain transactions, computing millions of replicated tokens in the blocks to sign and verify the digital wallet's ownership is computationally expensive. However, a certain of chosen arithmetical transformations that can simplify mathematical cost can significantly reduce computational complexity. This research's main contribution is developing a protocol that can reduce the complexity and mathematical cost in generating the digital wallet and verifying its authenticity of ownership. Finally, performance analyses of the RSA algorithm for the protocol have been measured and visualized using Python.
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Wunschmann, Jurgen, Sebastian Zanker, Christian Gunter, and Albrecht Rothermel. "Reduction of computational cost for high quality video scaling." IEEE Transactions on Consumer Electronics 56, no. 4 (November 2010): 2584–91. http://dx.doi.org/10.1109/tce.2010.5681144.

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Urriza, Jose M., Francisco E. Paez, Javier D. Orozco, and Ricardo Casyssials. "Computational Cost Reduction for Real-Time Schedulability Tests Algorithms." IEEE Latin America Transactions 13, no. 12 (December 2015): 3714–23. http://dx.doi.org/10.1109/tla.2015.7404899.

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Ono, Kiminori, Yoshiya Matsukawa, Aki Watanabe, Kazuki Dewa, Yasuhiro Saito, Yohsuke Matshushita, Hideyuki Aoki, Koki Era, Takayuki Aoki, and Togo Yamaguchi. "Computational Cost Reduction and Validation of Cluster-Cluster Aggregation Model." Journal of the Society of Powder Technology, Japan 52, no. 8 (2015): 426–34. http://dx.doi.org/10.4164/sptj.52.426.

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Drozdik, Martin, Youhei Akimoto, Hernan Aguirre, and Kiyoshi Tanaka. "Computational Cost Reduction of Nondominated Sorting Using the M-Front." IEEE Transactions on Evolutionary Computation 19, no. 5 (October 2015): 659–78. http://dx.doi.org/10.1109/tevc.2014.2366498.

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Singh, Satyavir, M. Abid Bazaz, and Shahkar Ahmad Nahvi. "A scheme for comprehensive computational cost reduction in proper orthogonal decomposition." Journal of Electrical Engineering 69, no. 4 (August 1, 2018): 279–85. http://dx.doi.org/10.2478/jee-2018-0039.

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Abstract This paper addresses the issue of offline and online computational cost reduction of the proper orthogonal decomposition (POD) which is a popular nonlinear model order reduction (MOR) technique. Online computational cost is reduced by using the discrete empirical interpolation method (DEIM), which reduces the complexity of evaluating the nonlinear term of the reduced model to a cost proportional to the number of reduced variables obtained by POD: this is the POD-DEIM approach. Offline computational cost is reduced by generating an approximate snapshot-ensemble of the nonlinear dynamical system, consequently, completely avoiding the need to simulate the full-order system. Two snapshot ensembles: one of the states and the other of the nonlinear function are obtained by simulating the successive linearization of the original nonlinear system. The proposed technique is applied to two benchmark large-scale nonlinear dynamical systems and clearly demonstrates comprehensive savings in computational cost and time with insignificant or no deterioration in performance.
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Ruiz, P. A., C. R. Philbrick, and P. W. Sauer. "Modeling Approaches for Computational Cost Reduction in Stochastic Unit Commitment Formulations." IEEE Transactions on Power Systems 25, no. 1 (February 2010): 588–89. http://dx.doi.org/10.1109/tpwrs.2009.2036462.

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Martinelli, G., G. Orlandi, and P. Burrascano. "Reduction of computational cost in DFT implementation of FIR digital filters." Electronics Letters 21, no. 9 (1985): 364. http://dx.doi.org/10.1049/el:19850260.

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Singh, Satyavir, Mohammad Abid Bazaz, and Shahkar Ahmad Nahvi. "Simulating swing dynamics of a power system model using nonlinear model order reduction." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 38, no. 6 (October 24, 2019): 1918–30. http://dx.doi.org/10.1108/compel-08-2018-0331.

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Purpose The purpose of this paper is to demonstrate the applicability of the Discrete Empirical Interpolation method (DEIM) for simulating the swing dynamics of benchmark power system problems. The authors demonstrate that considerable savings in computational time and resources are obtained using this methodology. Another purpose is to apply a recently developed modified DEIM strategy with a reduced on-line computational burden on this problem. Design/methodology/approach On-line computational cost of the power system dynamics problem is reduced by using DEIM, which reduces the complexity of the evaluation of the nonlinear function in the reduced model to a cost proportional to the number of reduced modes. The on-line computational cost is reduced by using an approximate snap-shot ensemble to construct the reduced basis. Findings Considerable savings in computational resources and time are obtained when DEIM is used for simulating swing dynamics. The on-line cost implications of DEIM are also reduced considerably by using approximate snapshots to construct the reduced basis. Originality/value Applicability of DEIM (with and without approximate ensemble) to a large-scale power system dynamics problem is demonstrated for the first time.
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Manis, George, Md Aktaruzzaman, and Roberto Sassi. "Low Computational Cost for Sample Entropy." Entropy 20, no. 1 (January 13, 2018): 61. http://dx.doi.org/10.3390/e20010061.

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Sample Entropy is the most popular definition of entropy and is widely used as a measure of the regularity/complexity of a time series. On the other hand, it is a computationally expensive method which may require a large amount of time when used in long series or with a large number of signals. The computationally intensive part is the similarity check between points in m dimensional space. In this paper, we propose new algorithms or extend already proposed ones, aiming to compute Sample Entropy quickly. All algorithms return exactly the same value for Sample Entropy, and no approximation techniques are used. We compare and evaluate them using cardiac inter-beat (RR) time series. We investigate three algorithms. The first one is an extension of the k d -trees algorithm, customized for Sample Entropy. The second one is an extension of an algorithm initially proposed for Approximate Entropy, again customized for Sample Entropy, but also improved to present even faster results. The last one is a completely new algorithm, presenting the fastest execution times for specific values of m, r, time series length, and signal characteristics. These algorithms are compared with the straightforward implementation, directly resulting from the definition of Sample Entropy, in order to give a clear image of the speedups achieved. All algorithms assume the classical approach to the metric, in which the maximum norm is used. The key idea of the two last suggested algorithms is to avoid unnecessary comparisons by detecting them early. We use the term unnecessary to refer to those comparisons for which we know a priori that they will fail at the similarity check. The number of avoided comparisons is proved to be very large, resulting in an analogous large reduction of execution time, making them the fastest algorithms available today for the computation of Sample Entropy.
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Dissertations / Theses on the topic "Computational Cost Reduction"

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Sahinkaya, Alican. "Computational Cost Reduction of Robust Controllers for Active Magnetic Bearing Systems." Cleveland State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=csu1592830264947936.

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Rui, Chen Bo, and 陳柏瑞. "Source Separation in Frequency Domain: Computation Cost Reduction and Sound Quality Enhancement." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/rntq3v.

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碩士
國立清華大學
電機工程學系
105
In a real environment, sound sources are mixed through convolution mixture, and it is difficult to separate sources in the time domain. Therefore, we use independent component analysis (ICA) in the frequency domain. Using ICA in the frequency domain could reduce the computation, but there are two important ambiguities: scaling problem and permutation problem. These ambiguities affect reconstruction of separated source. In this thesis, a new approach is proposed for solving the scaling problem and permutation problem. Besides, Time difference of arrival (TDOA) is used to confirm that two sources exist simultaneously. To solve the scaling problem, the Gaussian mixture model is uesd to approximate the distribution of the separated signal and the mixed signal. The difference between the mean of separated signal and the mean of the mixed signal is compensated to solve the scaling problem. Considering the permutation problem, the present algorithm relies on the assumption that the correlations should be high between the temporal envelopes of neighboring frequencies from the same sound source. First, we find the five neighboring frequency bins which have a high correlation with each other as a standard. After that, separated source in other frequency bins could confirm permutation through the correlation with the standard. We compare with the result of the approach of [30]. Computation time is reduced by 17 seconds and SIR enhances by 4 dB. In the part of the questionnaire, we get a higher score than [30]. 66 subjects were recruited to conduct a listening comprehension test. The accuracy of listening comprehension of separated sources is 41%, 26%, 45% higher than unprocessed sounds. The results show that our approach reduces computation cost and enhances sound quality when compared to the existing method [30].
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Singh, Inderjeet 1978. "Risk-averse periodic preventive maintenance optimization." Thesis, 2011. http://hdl.handle.net/2152/ETD-UT-2011-08-4203.

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We consider a class of periodic preventive maintenance (PM) optimization problems, for a single piece of equipment that deteriorates with time or use, and can be repaired upon failure, through corrective maintenance (CM). We develop analytical and simulation-based optimization models that seek an optimal periodic PM policy, which minimizes the sum of the expected total cost of PMs and the risk-averse cost of CMs, over a finite planning horizon. In the simulation-based models, we assume that both types of maintenance actions are imperfect, whereas our analytical models consider imperfect PMs with minimal CMs. The effectiveness of maintenance actions is modeled using age reduction factors. For a repairable unit of equipment, its virtual age, and not its calendar age, determines the associated failure rate. Therefore, two sets of parameters, one describing the effectiveness of maintenance actions, and the other that defines the underlying failure rate of a piece of equipment, are critical to our models. Under a given maintenance policy, the two sets of parameters and a virtual-age-based age-reduction model, completely define the failure process of a piece of equipment. In practice, the true failure rate, and exact quality of the maintenance actions, cannot be determined, and are often estimated from the equipment failure history. We use a Bayesian approach to parameter estimation, under which a random-walk-based Gibbs sampler provides posterior estimates for the parameters of interest. Our posterior estimates for a few datasets from the literature, are consistent with published results. Furthermore, our computational results successfully demonstrate that our Gibbs sampler is arguably the obvious choice over a general rejection sampling-based parameter estimation method, for this class of problems. We present a general simulation-based periodic PM optimization model, which uses the posterior estimates to simulate the number of operational equipment failures, under a given periodic PM policy. Optimal periodic PM policies, under the classical maximum likelihood (ML) and Bayesian estimates are obtained for a few datasets. Limitations of the ML approach are revealed for a dataset from the literature, in which the use of ML estimates of the parameters, in the maintenance optimization model, fails to capture a trivial optimal PM policy. Finally, we introduce a single-stage and a two-stage formulation of the risk-averse periodic PM optimization model, with imperfect PMs and minimal CMs. Such models apply to a class of complex equipment with many parts, operational failures of which are addressed by replacing or repairing a few parts, thereby not affecting the failure rate of the equipment under consideration. For general values of PM age reduction factors, we provide sufficient conditions to establish the convexity of the first and second moments of the number of failures, and the risk-averse expected total maintenance cost, over a finite planning horizon. For increasing Weibull rates and a general class of increasing and convex failure rates, we show that these convexity results are independent of the PM age reduction factors. In general, the optimal periodic PM policy under the single-stage model is no better than the optimal two-stage policy. But if PMs are assumed perfect, then we establish that the single-stage and the two-stage optimization models are equivalent.
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Books on the topic "Computational Cost Reduction"

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García, Roberto. The role of computational fluid dynamics at Marshall Space Flight Center: Presented at Emerging Horizons of Turbomachinery Technology, Wilder, Vermont, May 11-15, 1998. [Marshall Space Flight Center, Ala.]: Fluid Dynamics Analysis Branch, NASA/MSFC, 1998.

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Basu, Sanjay. Optimization. Edited by Sanjay Basu. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190667924.003.0003.

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This chapter seeks to determine how can we best allocate limited resources among many different programs. Given a budget to run a program that must be distributed among many different alternative projects (e.g., within a public health department, we might allocate some resources to a vaccination program, another set of resources to a diabetes prevention program, and yet another set of resources to an air pollution reduction program), how can we try to maximize the chances that we allocate limited resources fairly, ensuring that each program has at least the minimal resources that it needs while also ensuring that the distribution of resources maximizes overall public health? This chapter uses computational tools to solve such problems, focusing on how we can make smart decisions to maximize the potential effectiveness or cost-effectiveness of any particular program we’re interested in supporting—a goal known as optimization.
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Book chapters on the topic "Computational Cost Reduction"

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Cao, Lei, Jian Cao, and Minglu Li. "Genetic Algorithm Utilized in Cost-Reduction Driven Web Service Selection." In Computational Intelligence and Security, 679–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11596981_100.

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Bluemke, Ilona, and Karol Kulesza. "Reduction of Computational Cost in Mutation Testing by Sampling Mutants." In New Results in Dependability and Computer Systems, 41–51. Heidelberg: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00945-2_4.

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Dalvin Vinoth Kumar, A., P. D. Sheba Kezia Malarchelvi, and L. Arockiam. "CALDUEL: Cost And Load overhead reDUction for routE discovery in LOAD ProtocoL." In Advances in Computer and Computational Sciences, 229–37. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3770-2_21.

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Meijer, M. "Reduction of computational cost in fluid-structure interaction modelling using piston theory." In Insights and Innovations in Structural Engineering, Mechanics and Computation, 599–604. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2016. http://dx.doi.org/10.1201/9781315641645-99.

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Kim, In-Cheol, and Sung-Il Chien. "Computational Cost Reduction by Selective Attention for Fast Speaker Adaptation in Multilayer Perceptron." In Developments in Applied Artificial Intelligence, 17–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-48035-8_3.

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Pons-Prats, Jordi, Marti Coma, Jaume Betran, Xavier Roca, and Gabriel Bugeda. "Industrial Application of Genetic Algorithms to Cost Reduction of a Wind Turbine Equipped with a Tuned Mass Damper." In Computational Methods in Applied Sciences, 419–36. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89890-2_27.

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Morito, Takayuki, Osamu Sugiyama, Satoshi Uemura, Ryosuke Kojima, and Kazuhiro Nakadai. "Reduction of Computational Cost Using Two-Stage Deep Neural Network for Training for Denoising and Sound Source Identification." In Trends in Applied Knowledge-Based Systems and Data Science, 562–73. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42007-3_49.

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Baek, Seulkee, Mario Carneiro, and Marijn J. H. Heule. "A Flexible Proof Format for SAT Solver-Elaborator Communication." In Tools and Algorithms for the Construction and Analysis of Systems, 59–75. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72016-2_4.

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AbstractWe introduce , a new proof format for unsatisfiable SAT problems, and its associated toolchain. Compared to , the format allows solvers to include more information in proofs to reduce the computational cost of subsequent elaboration to . The format is easy to parse forward and backward, and it is extensible to future proof methods. The provision of optional proof steps allows SAT solver developers to balance implementation effort against elaboration time, with little to no overhead on solver time. We benchmark our toolchain against a comparable toolchain and confirm >84% median reduction in elaboration time and >94% median decrease in peak memory usage.
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Chan, Bun, and Leo Radom. "Accurate quadruple-ζ basis-set approximation for double-hybrid density functional theory with an order of magnitude reduction in computational cost." In Highlights in Theoretical Chemistry, 277–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-662-47051-0_25.

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Zdybał, K., M. R. Malik, A. Coussement, J. C. Sutherland, and A. Parente. "Reduced-Order Modeling of Reacting Flows Using Data-Driven Approaches." In Lecture Notes in Energy, 245–78. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-16248-0_9.

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AbstractData-driven modeling of complex dynamical systems is becoming increasingly popular across various domains of science and engineering. This is thanks to advances in numerical computing, which provides high fidelity data, and to algorithm development in data science and machine learning. Simulations of multicomponent reacting flows can particularly profit from data-based reduced-order modeling (ROM). The original system of coupled partial differential equations that describes a reacting flow is often large due to high number of chemical species involved. While the datasets from reacting flow simulation have high state-space dimensionality, they also exhibit attracting low-dimensional manifolds (LDMs). Data-driven approaches can be used to obtain and parameterize these LDMs. Evolving the reacting system using a smaller number of parameters can yield substantial model reduction and savings in computational cost. In this chapter, we review recent advances in ROM of turbulent reacting flows. We demonstrate the entire ROM workflow with a particular focus on obtaining the training datasets and data science and machine learning techniques such as dimensionality reduction and nonlinear regression. We present recent results from ROM-based simulations of experimentally measured Sandia flames D and F. We also delineate a few remaining challenges and possible future directions to address them. This chapter is accompanied by illustrative examples using the recently developed Python software, PCAfold. The software can be used to obtain, analyze and improve low-dimensional data representations. The examples provided herein can be helpful to students and researchers learning to apply dimensionality reduction, manifold approaches and nonlinear regression to their problems. The Jupyter notebook with the examples shown in this chapter can be found on GitHub at https://github.com/kamilazdybal/ROM-of-reacting-flows-Springer.
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Conference papers on the topic "Computational Cost Reduction"

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Ali, Md Belayet, Takashi Hirayama, Katsuhisa Yamanaka, and Yasuaki Nishitani. "Quantum Cost Reduction of Reversible Circuits Using New Toffoli Decomposition Techniques." In 2015 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2015. http://dx.doi.org/10.1109/csci.2015.41.

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Podkorytov, Andrey, and Anatoly Ryapukhin. "The reduction of computational cost in GNSS data network processing." In PROCEEDINGS OF THE II INTERNATIONAL CONFERENCE ON ADVANCES IN MATERIALS, SYSTEMS AND TECHNOLOGIES: (CAMSTech-II 2021). AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0092984.

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Arribas, Javier, Pau Closas, and Carles Fernandez-Prades. "Joint acquisition strategy of GNSS satellites for computational cost reduction." In European Workshop on GNSS Signals and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/navitec.2010.5708004.

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Fouladinejad, Nariman, Mohamad Kasim Abdul Jalil, and Jamaludin Mohd Taib. "Reduction of computational cost in driving simulation subsystems using approximation techniques." In 2014 International Conference on Industrial Automation, Information and Communications Technology (IAICT). IEEE, 2014. http://dx.doi.org/10.1109/iaict.2014.6922100.

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Sahinkaya, Alican, Larry Hawkins, and Jerzy T. Sawicki. "Computational Cost Reduction of MIMO Controllers for Active Magnetic Bearing Systems." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-15241.

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Abstract Generalized MIMO controllers such as H∞ and μ have not been widely adopted in the magnetic bearing industry, partially due to high computational cost relative to simpler SISO schemes. Computational cost is important to industrial magnetic bearing vendors as their controller hardware is based on embedded processors that have limited bandwidth. Studies to mitigate the problem of high order controllers show the limit of the existing methods in order reduction while still maintaining satisfying robust performance. A novel method is proposed to reduce the computational cost of robust controllers by identifying the bounds in their dynamic response, such that an implementation of a controller within those bounds results in the robust performance. The bounds are used to develop two computational cost reduction schemes for controller implementation, i.e., 1) identifying a dual-rate implementation of a single-rate controller which uniformly reduces the computational cost via interlacing technique, and 2) redesign of a controller by identifying its negligible dynamics based on the identified bounds in the controllers’ dynamic response. The results of both approaches are demonstrated on two active magnetic bearing (AMB) systems, a model of a 300 kW turbine generator with permanent magnet biased AMBs and an experimental high-speed AMB machining spindle. μ-synthesis controllers are designed for both systems and the proposed method and schemes are applied accordingly. The comparison of standard implementation of the synthesized controllers and the proposed new implementations is presented. The results demonstrate considerable reduction in the computational cost in terms of required number of multiply-accumulate (MAC) operations.
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Wang, Teng, Pere L. Gilabert, and Gabriel Montoro. "Under-sampling effects and computational cost reduction in RF power amplifier behavioral modeling." In 2015 10th European Microwave Integrated Circuits Conference (EuMIC). IEEE, 2015. http://dx.doi.org/10.1109/eumic.2015.7345067.

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Munoz-Jimenez, Vianney, and Anissa Zergainoh. "Computational cost reduction of H.264/AVC video coding standard for videoconferencing applications." In 2007 9th International Symposium on Signal Processing and Its Applications (ISSPA). IEEE, 2007. http://dx.doi.org/10.1109/isspa.2007.4555508.

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Soroushian, Aram, Amin Jahani Mehrnoosh, Yalda Zarabimanesh, Mohammad Hadi Ghondaghsaz, Ali Baiani, and Ali Zakizade. "ON THE PERFORMANCE OF A COMPUTATIONAL COST REDUCTION TECHNIQUE APPLIED TO COOLING TOWERS TRANSIENT ANALYSIS." In VII European Congress on Computational Methods in Applied Sciences and Engineering. Athens: Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece, 2016. http://dx.doi.org/10.7712/100016.1896.11470.

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Soroushian, Aram, Mahmood Hosseini, and Seyed Mohammad Khalkhali. "ON THE FREQUENCY CONTENT OF ERRORS ORIGINATED IN A TIME INTEGRATION COMPUTATIONAL COST REDUCTION TECHNIQUE." In VII European Congress on Computational Methods in Applied Sciences and Engineering. Athens: Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece, 2016. http://dx.doi.org/10.7712/100016.1898.12109.

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Sabir, Sameer, Sousso Kelouwani, Sayed Saeed Hosseini, Nilson Henao, Michael Fournier, and Kodjo Agbossou. "A Bid Selection Model for Computational Cost Reduction of Transactive Energy Aggregator in Smart Grids." In 2022 North American Power Symposium (NAPS). IEEE, 2022. http://dx.doi.org/10.1109/naps56150.2022.10012251.

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